This paper presents a novel wavelet-based denoising method using coefficient thresholding technique. The proposed method uses the adaptive thresholding which overcome the shortcomings of discontinuous function in hard-thresholding and also can eliminate the permanent bias in soft-thresholding. The qualitative evaluation of the denoising performance has shown that the proposed method cancels noi...

Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding methods using neighborhood processing is presented. Local adaptive techniques are more effective in eliminating both uneven lighting disturbance, noise and ghost objects. In order to demonstrate the effectiveness, locally adaptive thresholding methods namely Niblack, S...

Thresholding rules recently became of considerable interest when Donoho and John-stone applied them in the wavelet shrinkage context. Analytically simple, such rules are very eecient in data denoising and data compression problems. In this paper we nd hard thresholding decision rules that minimize Bayes risk for broad classes of underlying models. Standard Donoho-Johnstone test signals are used...

Wavelet thresholding is a powerful tool for denoising images and other signals with sharp discontinuities. Using different wavelet bases gives different results, and since the wavelet transform is not time-invariant, thresholding various shifts of the signal is one way to use different wavelet bases. This paper describes several denoising methods that apply wavelet thresholding or variations on...

Matrix completion (MC) is widely used in machine learning and signal processing to fill the missing data of an incomplete observation matrix. Singular value thresholding (SVT) one most popular algorithms among numerous MC methods. A Python-based GPU-accelerated SVT software presented this paper. It a user-friendly package minimise nuclear norm with high accuracy computational efficiency. Its ar...

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surf...